Method and system for predicting human congnitive performance

ABSTRACT

An apparatus and method for predicting cognitive performance of an individual based on factors including preferably sleep history and the time of day. The method facilitates the creation of predicted cognitive performance curves that allow an individual to set his/her sleep times to produce higher levels of cognitive performance. The method also facilitates the reconstruction of past cognitive performance levels based on sleep history.

[0001] This application is a continuation of U.S. application Ser. No.09/844,434, filed on Apr. 30, 2001, which is a continuation-in-part ofPCT Application No. PCT/US99/20092, filed Sep. 3, 1999 (which designatesthe United States and was published on May 11, 2000), which claimspriority from U.S. provisional Application Serial No. 60/106,419, filedOct. 30, 1998, and U.S. provisional Application Serial No. 60/122,407,filed Mar. 2, 1999; and U.S. application Ser. No. 09/844,434 claims thebenefit of U.S. provisional Application Serial No. 60/273,540, filedMar. 7, 2001. These patent applications are hereby incorporated byreference.

FIELD OF THE INVENTION

[0002] This invention relates to a method for predicting cognitiveperformance of an individual preferably based on that individual's priorsleep/wake history, the time of day, and tasks (or activities) beingperformed by the individual.

BACKGROUND OF THE INVENTION

[0003] Maintenance of productivity in any workplace setting depends uponeffective cognitive performance at all levels from command/control ormanagement down to the individual soldier or worker. Effective cognitiveperformance in turn depends upon complex mental operations. Many factorshave been shown to affect cognitive performance (e.g., drugs or age).However, of the numerous factors causing day to day variations incognitive performance, two have been shown to have the greatest impact.These two factors are an individual's prior sleep/wake history and thetime of day.

[0004] Adequate sleep sustains cognitive performance. With less thanadequate sleep, cognitive performance degrades over time. An article byThorne et al. entitled “Plumbing Human Performance Limits During 72hours of High Task Load” in Proceedings of the 24^(th) DRG Seminar onthe Human as a Limiting Element in Military Systems, Defense and CivilInstitute of Environmental Medicine, pp. 17-40 (1983), an article byNewhouse et al. entitled “The Effects of d-Amphetamine on Arousal,Cognition, and Mood After Prolonged Total Sleep Deprivation” publishedin Neuropsychopharmacology, vol. 2, pp. 153-164 (1989), and anotherarticle by Newhouse et al. entitled “Stimulant Drug Effects onPerformance and Behavior After Prolonged Sleep Deprivation: A Comparisonof Amphetamine, Nicotine, and Deprenyl” published in MilitaryPsychology, vol. 4, pp. 207-233 (1992) all describe studies of normalvolunteers in which it is revealed that robust, cumulative decrements incognitive performance occur during continuous total sleep deprivation asmeasured by computer-based testing and complex operational simulation.In the Dinges et al. article entitled “Cumulative Sleepiness, MoodDisturbance, and Psychomotor Vigilance Performance Decrements During aWeek of Sleep Restricted to 4-5 Hours Per Night” published in Sleep,vol. 20, pp. 267-277 (1997), it is revealed that on fixed, restricteddaily sleep amounts, cumulative reduced sleep also leads to a cognitiveperformance decline. Thus, in operational settings, both civilian andmilitary, sleep deprivation reduces productivity (output of useful workper unit of time) on cognitive tasks.

[0005] Thus, using computer-based cognitive performance tests, it hasbeen shown that total sleep deprivation degrades human cognitiveperformance by approximately 25% for each successive period of 24 hoursawake. However, it also has been shown that even small amounts of sleepreduce the rate of sleep loss-induced cognitive performance degradation.Belenky et al. in their article entitled “Sustaining Performance DuringContinuous Operations: The U.S. Army's Sleep Management System,”published in 20^(th) Army Science Conference Proceedings, vol. 2, pp.657-661 (1996) disclose that a single 30-minute nap every 24 hoursreduces the rate of cognitive performance degradation to 17% per dayover 85 hours of sleep deprivation. This suggests that recuperation ofcognitive performance during sleep accrues most rapidly early in thesleep period. No other factor besides the amount of sleep contributes sosubstantially and consistently to the normal, daily variations incognitive performance.

[0006] In addition to sleep/wake history, an individual's cognitiveperformance at a given point in time is determined by the time of day.In the early 1950s, Franz Halberg and associates observed a 24-hourperiodicity in a host of human physiologic (including body temperatureand activity), hematologic, and hormonal functions, and coined the term‘circadian’ (Latin for ‘about a day’) to describe this cyclic rhythm.Halberg showed that most noise in experimental data came fromcomparisons of data sampled at different times of day.

[0007] When humans follow a nocturnal sleep/diurnal wake schedule (forexample, an 8-hour sleep/16-hour wake cycle, with nightly sleepcommencing at approximately midnight), body temperature reaches aminimum (trough) usually between 2:00 AM and 6:00 AM. Body temperaturethen begins rising to a maximum (peak) usually between 8:00 PM and 10:00PM. Likewise, systematic studies of daily human cognitive performancerhythms show that speed of responding slowly improves across the day toreach a maximum in the evening (usually between 8:00 PM and 10:00 PM)then dropping more rapidly to a minimum occurring in the early morninghours (usually between 2:00 AM and 6:00 AM). Similar but somewhat lessconsistent rhythms have been shown from testing based on variouscognitive performance tasks. Thus, superimposed on the effect of totalsleep deprivation on cognitive performance noted above was anapproximately ±10% variation in cognitive performance over each 24-hourperiod.

[0008] Various measures have been shown to correlate, to some extent,with cognitive performance. These include objective and subjectivemeasures of sleepiness (or its converse, alertness). Some individualsfamiliar with the art use “sleepiness” to indicate the opposite of“alertness” (as is the case in the present document). “Drowsiness” oftenis used interchangeably with “sleepiness” although some familiar withthe art would argue that “sleepiness” pertains specifically to thephysiological need for sleep whereas “drowsiness” refers more to thepropensity or ability to fall asleep (independent of physiological sleepneed) or the subjective feeling of lack of alertness. The term “fatigue”has been used as a synonym for “sleepiness” by the lay population, butthose familiar with the art do not consider “fatigue” to beinterchangeable with “sleepiness”—rather, “fatigue” is a broad term thatencompasses more than just the effects of sleep loss per se onperformance. Likewise, “cognitive performance” has been defined asperformance on a wide variety of tasks, the most commonly used beingvigilance tasks (tasks requiring sustained attention). From vigilanceand other tasks, some researchers use accuracy as their measure ofcognitive performance, while others use reaction time (or its inverse,speed). Still others use a measure that is calculated as speedmultiplied by accuracy, that is the amount of useful work performed perunit of time (also known as throughput). Those familiar with the artgenerally agree that vigilance tasks are appropriate measures ofcognitive performance under conditions of sleep deprivation, and thateither reaction time (speed) or some measure that takes reaction timeinto account (e.g., throughput) is a valid and reliable way of measuringcognitive performance.

[0009] The Multiple Sleep Latency Test (MSLT) is a widely acceptedobjective measure of sleepiness/alertness. In the MSLT, individuals tryto fall asleep while lying in a darkened, quiet bedroom. Variousphysiological measures used to determine sleep or wakefulness arerecorded (eye movements, brain activity, muscle tone), and time taken toreach the first 30 seconds of stage 1 (light) sleep is determined.Shorter latencies to stage 1 are considered to indicate greatersleepiness (lower alertness). Sleep latencies under 5 minutes areconsidered to be pathological (i.e., indicative of a sleep disorder orsleep deprivation). During both total and partial sleep deprivation,latency to sleep on the MSLT (alertness) and performance decline (i.e.,sleepiness as measured by MSLT increases). However, although there is acorrelation between MSLT-determined sleepiness/alertness and cognitiveperformance (greater sleepiness as indexed by MSLT corresponding topoorer cognitive performance), this correlation has never been shown tobe perfect and for the most part is not strong. As a result, the MSLT isa poor (i.e., unreliable) predictor of cognitive performance.

[0010] Subjective measures of sleepiness/alertness also have been shownto correlate (albeit weakly) with cognitive performance. Hoddes et al.in their article entitled “Quantification of Sleepiness: A New Approach”published in Psychophysiology, vol. 10, pp. 431-436 (1973) describe theStanford Sleepiness Scale (SSS), a subjective questionnaire used widelyto measure sleepiness/alertness. In the SSS, individuals rate theircurrent level of sleepiness/alertness on a scale from 1 to 7, with 1corresponding to the statement, “feeling active and vital; alert; wideawake” and 7 corresponding to the statement “almost in reverie; sleeponset soon; losing struggle to remain awake.” Higher SSS scores indicategreater sleepiness. As with the MSLT, during both total and partialsleep deprivation, scores on the SSS increase. However, as with MSLT,the correspondence between SSS-determined sleepiness/alertness andcognitive performance decrements is weak and inconsistent. As a result,the SSS also is a poor predictor of cognitive performance. Some otherexamples of subjective measures of sleepiness/alertness include theEpworth Sleepiness Scale described by Johns in his article entitled“Daytime Sleepiness, Snoring, and Obstructive Sleep Apnea” published inChest, vol. 103, pp. 30-36 (1993) and the Karolinska Sleepiness scaledescribed by Akerstedt and Gillberg in their article entitled“Subjective and Objective Sleepiness in the Active Individual” publishedin International Journal of Neuroscience, vol. 52, pp. 29-37 (1990). Thecorrespondence between these subjective measures and cognitiveperformance also is weak and inconsistent.

[0011] In addition, factors modifying cognitive performance may notcorrespondingly affect objective or subjective measures ofsleepiness/alertness, and vice versa. For example, the Penetar et al.article entitled “Amphetamine Effects on Recovery Sleep Following TotalSleep Deprivation” published in Human Psychopharmacology, vol. 6, pp.319-323 (1991) discloses that during sleep deprivation, the stimulantdrug d-amphetamine improved cognitive performance but notsleepiness/alertness (as measured by the MSLT). In a similar study,caffeine given as a sleep deprivation countermeasure maintained elevatedcognitive performance for over 12 hours while the effects on subjectivesleepiness, vigor and fatigue transiently improved but then decayed.Thorne et al. in their article entitled “Plumbing Human PerformanceLimits During 72 hours of High Task Load” in Proceedings of the 24^(th)DRG Seminar on the Human as a Limiting Element in Military Systems,Defense and Civil Institute of Environmental Medicine, pp. 17-40 (1983)describe how cognitive performance continues to decline over 72 hours ofsleep deprivation whereas subjective sleepiness/alertness declined overthe first 24 hours but subsequently leveled off. The findings thatcognitive performance and measures of sleepiness/alertness are notalways affected in the same way indicate that they are notinterchangeable. That is, measures of sleepiness/alertness cannot beused to predict cognitive performance, and vice versa.

[0012] Methods and apparatuses related to alertness detection fall intofive basic categories: a method/apparatus for unobtrusively monitoringcurrent alertness level; a method/apparatus for unobtrusively monitoringcurrent alertness level and providing a warning/alarm to the user ofdecreased alertness and/or to increase user's alertness level; amethod/apparatus for monitoring current alertness level based on theuser's responses to some secondary task possibly with an alarm device towarn the user of decreased alertness and/or to increase user's alertnesslevel; methods to increase alertness; and a method/apparatus forpredicting past, current, or future alertness.

[0013] These methods and apparatuses that unobtrusively monitor thecurrent alertness level are based on an “embedded measures” approach.That is, such methods infer alertness/drowsiness from the current levelof some factor (e.g., eye position or closure) assumed to correlate withalertness/drowsiness. Issued patents of this type include U.S. Pat. No.5,689,241 to J. Clarke, Sr., et al. disclosing an apparatus to detecteye closure and ambient temperature around the nose and mouth; U.S. Pat.No. 5,682,144 to K. Mannik disclosing an apparatus to detect eyeclosure; and U.S. Pat. No. 5,570,698 to C. Liang et al. disclosing anapparatus to monitor eye localization and motion to detect sleepiness.An obvious disadvantage of these types of methods and apparatuses isthat the measures are likely detecting sleep onset itself rather thansmall decreases in alertness.

[0014] In some patents, methods for embedded monitoring ofalertness/drowsiness are combined with additional methods for signalingthe user of decreased alertness and/or increasing alertness. Issuedpatents of this type include U.S. Pat. No. 5,691,693 to P. Kithildescribing a device that senses a vehicle operator's head position andmotion to compare current data to profiles of “normal” head motion and“impaired” head motion. Warning devices are activated when head motiondeviates from the “normal” in some predetermined way. U.S. Pat. No.5,585,785 to R. Gwin et al. describes an apparatus and a method formeasuring total handgrip pressure on a steering wheel such that an alarmis sounded when the grip pressure falls below a predetermined “lowerlimit” indicating drowsiness. U.S. Pat. No. 5,568,127 to H. Bangdescribes a device for detecting drowsiness as indicated by the user'schin contacting an alarm device, which then produces a tactile andauditory warning. U.S. Pat. No. 5,566,067 to J. Hobson et al. describesa method and an apparatus to detect eyelid movements. A change indetected eyelid movements from a predetermined threshold causes anoutput signal/alarm (preferably auditory). As with the first category ofmethods and apparatuses, a disadvantage here is that the measures arelikely detecting sleep onset itself rather than small decreases inalertness.

[0015] Other alertness/drowsiness monitoring devices have been developedbased on a “primary/secondary task” approach. For example, U.S. Pat. No.5,595,488 to E. Gozlan et al. describes an apparatus and a method forpresenting auditory, visual, or tactile stimuli to an individual towhich the individual must respond (secondary task) while performing theprimary task of interest (e.g., driving). Responses on the secondarytask are compared to baseline “alert” levels for responding. U.S. Pat.No. 5,259,390 to A. MacLean describes a device in which the userresponds to a relatively innocuous vibrating stimulus. The speed torespond to the stimulus is used as a measure of the alertness level. Adisadvantage here is that the apparatus requires responses to asecondary task to infer alertness, thereby altering and possiblyinterfering with the primary task.

[0016] Other methods exist solely for increasing alertness, dependingupon the user to self-evaluate alertness level and activate the devicewhen the user feels drowsy. An example of the latter is U.S. Pat. No.5,647,633 and related patents to M. Fukuoka in which a method/apparatusis described for causing the user's seat to vibrate when the userdetects drowsiness. Obvious disadvantages of such devices are that theuser must be able to accurately self-assess his/her current level ofalertness, and that the user must be able to correctly act upon thisassessment.

[0017] Methods also exist to predict alertness level based on userinputs known empirically to modify alertness. U.S. Pat. No. 5,433,223 toM. Moore-Ede et al. describes a method for predicting the likelyalertness level of an individual at a specific point in time (past,current or future) based upon a mathematical computation of a variety offactors (referred to as “real-world” factors) that bear somerelationship to alterations in alertness. The individual's BaselineAlertness Curve (BAC) is first determined based on five inputs andrepresents the optimal alertness curve displayed in a stableenvironment. Next, the BAC is modified by alertness modifying stimuli toarrive at a Modified Baseline Alertness Curve. Thus, the method is ameans for predicting an individual's alertness level, not cognitiveperformance.

[0018] Another method has been designed to predict “work-relatedfatigue” as a function of number of hours on duty. Fletcher and Dawsondescribe their method in an article entitled “A Predictive Model ofWork-Related Fatigue Based on Hours of Work” published in Journal ofOccupational Health and Safety, vol. 13, 471-485 (1997). In this model asimplifying assumption is made—it is assumed that length of on-duty timecorrelates positively with time awake. To implement the method, the userinputs a real or hypothetical on-duty/off-duty (work/rest) schedule.Output from the model is a score that indicates “work-related fatigue.”Although this “work-related fatigue” score has been shown to correlatewith some performance measures, it is not a direct measure of cognitiveperformance per se. It can be appreciated that the fatigue score will beless accurate under circumstances when the presumed relationship betweenon-duty time and time awake breaks down—for example when a person worksa short shift but then spends time working on projects at home ratherthan sleeping or when a person works long shifts but conscientiouslysleeps all the available time at home. Also, this method is obtrusive inthat the user must input on-duty/off-duty information rather than suchinformation being automatically extracted from an unobtrusive recordingdevice. In addition, the model is limited to predictions of “fatigue”based on work hours. Overall, this model is limited to work-relatedsituations in which shift length consistently correlates (inversely)with sleep length.

[0019] Given the importance of the amount of sleep and the time of dayfor determining cognitive performance (and hence estimating productivityor effectiveness), and given the ever-increasing requirements of mostoccupations on cognitive performance, it is desirable to design areliable and accurate method of predicting cognitive performance. It canbe appreciated that increasing the number of relevant inputs increasescognitive performance prediction accuracy. However, the relativebenefits gained from such inputs must be weighed against the additionalburdens/costs associated with their collection and input. For example,although certain fragrances have been shown to have alertness-enhancingproperties, these effects are inconsistent and negligible compared tothe robust effects of the individual's sleep/wake history and the timeof day. More important, the effect of fragrances on cognitiveperformance is unknown. Requiring an individual to keep a log ofexposure to fragrances would be time consuming to the individual andonly result in negligible gains in cognitive performance predictionaccuracy. In addition, while the effects of the sleep/wake history andthe time of day on cognitive performance are well known, the effects ofother putative alertness-altering factors (e.g., job stress), how tomeasure them (their operational definition), and their direction ofaction (cognitive performance enhancing or degrading) are virtuallyunknown.

[0020] An important and critical distinction between the presentinvention and the prior art is that the present invention is a model topredict performance on tasks with a cognitive component. In contrast,previous models involving sleep and/or circadian rhythms (approximately24-hour) focused on the prediction of “alertness” or “sleepiness.” Thelatter are concepts that specifically relate to the propensity toinitiate sleep, not the ability to perform a cognitive task.

[0021] Although sleepiness (or its converse, alertness) could be viewedas an intervening variable that can mediate cognitive performance, thescientific literature clearly shows that cognitive performance andalertness are conceptually distinct, as reviewed by Johns in the articleentitled “Rethinking the Assessment of Sleepiness” published in SleepMedicine Reviews, vol. 2, pp. 3-15 (1998) and as reviewed by Mitler etal. in the article entitled “Methods of Testing for Sleepiness”published in Behavioral Medicine, vol. 21, pp. 171-183 (1996). Thomas etal. in the article entitled “Regional Cerebral Metabolic Effects ofProlonged Sleep Deprivation” published in NeuroImage, vol. 7, p. S130(1998) reveal that 1-3 days of sleep loss result in reductions in globalbrain activation of approximately 6%, as measured by regional cerebralglucose uptake. However, those regions (heteromodal associationcortices) that mediate the highest order cognitive functions (includingbut not limited to attention, vigilance, situational awareness,planning, judgment, and decision making) are selectively deactivated bysleep loss to a much greater extent—up to 50%—after three days of sleeploss. Thus, decreases in neurobiological functioning during sleeprestriction/deprivation are directly reflected in cognitive performancedegradation. These findings are consistent with studies demonstratingthat tasks requiring higher-order cognitive functions, especially thosetasks requiring attention, planning, etc. (abilities mediated byheteromodal association areas) are especially sensitive to sleep loss.On the other hand, brain regions such as primary sensory regions, aredeactivated to a lesser degree. Concomitantly, performance (e.g.,vision, hearing, strength and endurance tasks) that is dependent onthese regions is virtually unaffected by sleep loss.

[0022] Consequently, devices or inventions that predict “alertness” perse (e.g., Moore-Ede et al.) putatively quantify the brain's underlyingpropensity to initiate sleep at any given point in time. That is,devices or inventions that predict “alertness” (or its converse“sleepiness”) predict the extent to which sleep onset is likely. Thepresent invention differs from such approaches in that the nature of thetask is accounted for i.e., it is not the propensity to initiate sleepthat is predicted. Rather, the present invention predicts the extent towhich performance of a particular task will be impaired by virtue of itsreliance upon brain areas most affected by sleep deprivation(heteromodal association areas of the brain). The most desirable methodwill produce a highly reliable and accurate cognitive performanceestimate based on the sleep/wake history of an individual, the time ofday, and the amount of time on a particular task.

SUMMARY OF THE INVENTION

[0023] A method for providing a cognitive performance level inaccordance with the invention includes receiving a data seriesrepresenting at least one wake state and at least one sleep state,selecting a function based on the data series, determining a cognitiveperformance capacity using the selected function, modulating thecognitive performance capacity with a time of day value, and providingthe modulated value.

[0024] An apparatus for providing a cognitive performance level inaccordance with the invention includes means for receiving a data serieshaving at least one wake state and at least one sleep state, means forselecting a function based on the data series, means for determining acognitive performance capacity using the selected function, means forstoring a series of time of day values, means for modulating thecognitive performance capacity with a corresponding time of day value,and means for providing the modulated value.

[0025] A method for determining a cognitive performance level inaccordance with the invention includes inputting a data series havingwake states and sleep states of an individual, selecting a functionbased on the wake states and sleep states in the data series,calculating a cognitive performance capacity based on the selectedfunction, modulating the cognitive performance capacity with a time ofday value, and outputting the modulated value as the predicted cognitiveperformance.

[0026] A method for determining a predicted cognitive performance inaccordance with the invention includes inputting a data series havingwake states and sleep states of an individual, selecting a functionbased on the wake states and sleep states in the data series,calculating a cognitive performance capacity based on the selectedfunction, approximating a first curve of calculated cognitiveperformance capacities, modulating the first curve with a second curverepresenting time of day rhythms, and outputting the modulated firstcurve representing the predicted cognitive performance.

[0027] A feature of the present invention is that it provides anumerical representation of predicted cognitive performance with animmediate ergonomic and economic advantage, i.e., an indication ofproductivity or effectiveness of an individual. Another feature of thepresent invention is that it does not require or usemeasurements/computations that are indirect, intermediate, inferentialor hypothetical concomitants of cognitive performance. Examples of thelatter are alertness, sleepiness, time to sleep onset, body temperatureand/or other physiological measures that vary with time. A furtherfeature of the invention is that it accounts for transient oradventitious variations in cognitive performance from any source as aresult of how that source affects the sleep/wake history (e.g., age)and/or physiological time of day (e.g., shift work). In effect, suchsources are not treated as having effects on cognitive performanceindependent of the sleep/wake history and/or the time of day, and assuch do not require separate measurement, tabulation, and input into themethod.

[0028] One objective of this invention is to provide an accurate methodfor predicting cognitive performance of an individual.

[0029] A further objective is to provide a method that facilitatesprediction of the effects of possible future sleep/wake histories oncognitive performance (forward prediction).

[0030] Another objective is to provide a method that facilitatesretrospective analysis of likely prior cognitive performance based on,for example, the individual's sleep/wake history, the time of day, andthe activities done by the individual.

[0031] Another objective is to provide a method for coordination andoptimization of available sleep/wake time in order to obtain net optimalpredicted cognitive performance for an individual and/or a group ofindividuals.

[0032] It can be appreciated that an implicit advantage and novelty ofthe method is its parsimony. The method uses those factors possessingmaximal predictive value (as demonstrated empirically) as continuouslyupdated inputs. Thus, the model will be simple to implement. Othermodels predicting “alertness” require the user to track multiple inputvariables (e.g., caffeine, alcohol ingestion, light/dark exposure,diurnal type), rather than presenting these inputs as optional“attachments” to a standard, simplified model based on those factorsaccounting for maximum cognitive performance change. For example, inaccordance with a segment of the present method, the effects of age oncognitive performance are accounted for implicitly via the empiricallyderived effects of age on sleep. That is, sleep quality degrades withage. The inherent degradation in sleep quality with aging wouldimplicitly result in a prediction of degraded cognitive performance(since in the present method degraded sleep results in a prediction ofdegraded cognitive performance), even if an individual's age wereunknown. Therefore, age need not constitute a separate (independent)input variable to a cognitive performance prediction model.

[0033] The invention also provides other significant advantages. Forexample, an advantage of this invention is the elimination of a need forempirical evaluation.

[0034] Another advantage of this invention is obtaining an accurateprediction of cognitive performance of an individual. The advantage maybe achieved by a method incorporating, for example, at least two of thefollowing factors that have been empirically demonstrated to exert asignificant effect on cognitive performance, namely, (1) theindividual's sleep/wake history, (2) the time of day (“day” hereinreferring to a 24hour period including both nighttime and daylighthours), and (3) the individual's time on a particular task.

[0035] Another advantage achieved by this invention is an accurateprediction of current cognitive performance.

[0036] Another advantage achieved by this invention is that it iscapable of providing a real time prediction of cognitive performance.

[0037] Yet another advantage achieved by this invention is a predictionof future expected cognitive performance throughout the day based onhypothetical future sleep/wake periods.

[0038] An additional advantage achieved by this invention is aretrospective analysis of cognitive performance at given times.

[0039] A further advantage of the invention is that a particularcognitive performance prediction is not based on normative data (i.e.,does not require a “look-up table” for output), but rather is calculateddirectly based on, for example as discussed in connection with oneembodiment, each individual's sleep/wake information, the time of day,and the time on a task.

[0040] A further advantage of the invention is that it can be used tooptimize the individual's future sleep/wake schedule based on a fixedmission/work schedule. Previous methods and apparatuses are directedtoward modifying the work schedule and/or mission to “fit theindividual.” In most situations, however, work schedules and/or missionsare fixed. Thus, modifying the work schedule or mission to suit theindividual is impractical or impossible. A more reasonable approachincorporated in the present method is to allow the individual to adjusthis/her sleep/wake periods to meet work/mission demands. Thus, thecurrent method presents a more practical alternative by providing ameans to regulate work hours to a directly applicable metric (cognitiveperformance) instead of regulating work hours by time off duty or byusing indirect measures of cognitive performance such as alertness.

[0041] A feature of this invention is the provision of a graphicalrepresentation that translates an individual's sleep/wake history andthe time of day into an immediately useful, self-evident index. Aprediction of cognitive performance, unlike a prediction of “alertness”or “sleepiness,” requires no further interpretation.

[0042] The method for predicting human cognitive performance inaccordance with the invention accomplishes the above objectives andachieves the above advantages. The method and resulting apparatus areeasily adapted to a wide variety of situations and types of inputs.

[0043] In accordance with an aspect of the invention, an individualsleep/wake history is inputted into a processing device. The processingdevice classifies the individual pieces of sleep/wake history data aseither sleep or wake. Based on the classification of data, theprocessing device selects and calculates a cognitive performancecapacity corresponding to the present state of the individual, thecognitive performance capacity may be modified by a time of day value toadjust the cognitive performance capacity to a predicted cognitiveperformance. The predicted cognitive performance represents the abilityof the individual to perform cognitive tasks. The predicted cognitiveperformance may be displayed for a real-time indication or as part of acurve, printed out with the information that could have been displayed,and/or stored for later retrieval and/or use. The calculation of thecognitive performance capacity is made based on functions that model theeffect of the interrelationship of sleep and being awake on cognitiveperformance. The time of day function models the effect of anindividual's circadian rhythms on cognitive performance.

[0044] In accordance with the underlying method of the invention, themethod can be accomplished with a wide variety of apparatus. Examples ofthe possible apparatus embodiments include electronic hardware as eitherdedicated equipment or equipment internal to a computer, softwareembodied in computer readable material for use by computers, softwareresident in memory or a programmed chip for use in computers ordedicated equipment, or some combination of both hardware and software.The dedicated equipment may be part of a larger device that wouldcomplement the dedicated equipment's purpose.

[0045] Given the following enabling description of the drawings, theinvention should become evident to a person of ordinary skill in theart.

DESCRIPTION OF THE DRAWINGS

[0046]FIG. 1(a) is a conceptual diagram representation of the inventionincluding the fine-tuning alternative embodiment. FIG. 1(b) graphicallyshows the combination of output from the functions represented by FIG.3(a) with time of day modulation to derive predicted cognitiveperformance.

[0047]FIG. 2 is a block diagram representation of the wake, sleep,delay, and sleep inertia functions for calculating predicted cognitiveperformance capacity.

[0048]FIG. 3(a) graphically illustrates the effect of being awake andasleep on cognitive performance capacity over a 24-hour period. FIG.3(b) is an enlarged view of circled portion 3(b) of FIG. 3(a), andgraphically shows the delay function with respect to cognitiveperformance capacity. FIG. 3(c) is an enlarged view of circled portion3(c) of FIG. 3(a), and graphically shows the sleep inertia function withrespect to cognitive performance capacity.

[0049] FIGS. 4(a)-(b) depict a detailed flowchart showing the steps ofthe method of the invention.

[0050]FIG. 5 illustrates time on task effects across a 10-minutePsychomotor Vigilance Task (PVT) sessions at two hour increments during40 hours of total sleep deprivation.

[0051]FIG. 6 depicts a functional representation of an alternativeembodiment.

[0052]FIG. 7(a) illustrates a block diagram of structural components forthe preferred embodiment. FIG. 7(b) illustrates a block diagram of analternative set of structural components.

[0053] FIGS. 8(a)-(b) depict a detailed flowchart showing the steps ofan alternative embodiment.

DETAILED DESCRIPTION OF THE INVENTION

[0054] The present invention now is described more fully hereinafterwith reference to the accompanying drawings, in which preferredembodiments of the invention are shown. This invention may, however, beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will be thorough and complete, and willfully convey the scope of the invention to those skilled in the art. Thepresent invention will now be described more fully hereinafter withreference to the accompanying drawings, in which preferred embodimentsof the invention are shown. Like numbers refer to like elementsthroughout.

[0055] As will be appreciated by one of skill in the art, the presentinvention may be embodied as a method, data processing system, orcomputer program product. Accordingly, the present invention may takethe form of an entirely hardware embodiment, an entirely softwareembodiment or an embodiment combining software and hardware aspects.Furthermore, the present invention may take the form of a computerprogram product on a computer-usable storage medium havingcomputerusable program code means embodied in the medium. Any suitablecomputer readable medium may be utilized including hard disks, CD-ROMs,optical storage devices, or magnetic storage devices.

[0056] Computer program code for carrying out operations of the presentinvention may be written in an object oriented programming language suchas Java®, Smalltalk or C++. However, the computer program code forcarrying out operations of the present invention may also be written inconventional procedural programming languages, such as the “C”programming language.

[0057] The program code may execute entirely on the user's computer, asa standalone software package; on a remote computer; or it may executepartly on the user's computer and partly on a remote computer. In thelatter scenario, the remote computer may be connected directly to theuser's computer through a LAN or a WAN (Intranet), or the connection maybe made indirectly through an external computer (for example, throughthe Internet using an Internet Service Provider). The invention may beimplemented as software that may be resident on a stand-alone devicesuch as a personal computer, a PAL device, a personal digital assistant(PDA), an e-book or other handheld or wearable computing devices(incorporating Palm OS, Windows CE, EPOC, or future generations likecode-named products Razor from 3Com or Bluetooth from a consortiumincluding IBM and Intel), or a specific purpose device having anapplication specific integrated circuit (ASIC).

[0058] The present invention is described below with reference toflowchart illustrations of methods, apparatus (systems) and computerprogram products according to an embodiment of the invention. It will beunderstood that each block of the flowchart illustrations, andcombinations of blocks in the flowchart illustrations, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing the functionsspecified in the flowchart block or blocks.

[0059] These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function specified in the flowchart block or blocks.Examples of how the software can be stored for use are the following: inrandom access memory (RAM); in read only memory (ROM); on a storagedevice like a hard drive, disk, compact disc, punch card, tape or othercomputer readable material; in virtual memory on a network, a computer,an intranet, the Internet, the Abilene Project, or otherwise; on anoptical storage device; on a magnetic storage device; and/or on anEPROM.

[0060] The computer program instructions may also be loaded onto acomputer or other programmable data processing apparatus to cause aseries of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer implemented process suchthat the instructions which execute on the computer or otherprogrammable apparatus provide steps for implementing the functionsspecified in the flowchart block or blocks.

[0061] The present invention involves a method for predicting cognitiveperformance at a given time in the past, present, or future as aconsequence of the amount of sleep and wakefulness up to that time as afunction of the time of day and the workload for a particularindividual. The method calculates a numerical estimate of cognitiveperformance for an individual as a continuous function of time. Thecalculations (described below) are based on empirically derived directmathematical relationships among (1) the continuous decrement ofcognitive performance during wakefulness; (2) restoration of cognitiveperformance during sleep; (3) cyclic variation in cognitive performanceduring the course of the day; and (4) variations in cognitiveperformance due to whether and what activities are occurring.

[0062] In accordance with the invention, a numeric value indicatingpredicted cognitive performance at a given moment in time is provided asshown in FIGS. 1(a)-(b). As shown in FIG. 1(a), predicted cognitiveperformance equals the output of a series of calculations and/ordeterminations obtained in three general steps, using functionsempirically derived from direct measurements of cognitive performanceunder scientifically controlled conditions. The first step, as shown inFIG. 2, preferably uses a set of functions to calculate an initial valuereferred to as the level of cognitive performance capacity asgraphically depicted in FIGS. 3(a)-(c). Once the level of cognitiveperformance capacity is calculated, the second step preferablycalculates or uses a previously calculated time of day modulator Mrepresented as G8 in FIG. 1(b) and S8 in FIG. 4(b). The third steppreferably calculates a task modulator T represented as S9-S10(b) inFIG. 4(b). Alternatively, the second and third steps may be switchedand/or combined. The fourth step preferably involves the mathematicalcombination of the results from the first through third steps yielding apredicted cognitive performance, shown as a block diagram in FIG. 1(a)and graphically represented in FIG. 1(b), which illustrates thecombination of the cognitive performance capacity and the time of daymodulator.

[0063] There are four functions relating to the sleep/wake history usedto calculate the level of cognitive performance capacity as shown inFIGS. 2-4(b). The wake function w(t) quantifies empirically derivedrelationships between the time awake and degradation of cognitiveperformance. The sleep function s(t) quantifies empirically derivedrelationships between the time asleep and maintenance and/orrecuperation of cognitive performance. In addition to these two primaryfunctions that operate during the bulk of the time awake or asleep thereare two other functions that operate briefly during the transition fromone state to the other. They include the delay of recuperation functiond(t) and the sleep inertia function i(t). The delay of recuperationfunction d(t) represents the relationship between the wake to sleeptransition and the recuperation of cognitive performance. This functionoperates during the initial period of sleep following being awake, knownas stage 1 sleep, as shown in FIG. 3(b). The sleep inertia function i(t)represents the relationship between the sleep to wake transition andcognitive performance. This function operates during the initial periodof time being awake after being asleep as shown in FIG. 3(c).

[0064] The function representing the time of day effects on cognitiveperformance is used to calculate a modulating factor M. The time of dayfunction describes empirically derived relationships between the time ofday (point in time within a 24-hour period) and the variation incognitive performance over the course of the day as exemplified by G8 inFIG. 1(b).

[0065] The function representing the task/activity impact on cognitiveperformance is used to calculate a modulating factor T. The taskfunction describes the impact of the performance of a task and/or anactivity upon cognitive performance preferably based upon, for example,the intensity, the length, the complexity, and the difficulty associatedwith the particular task and/or activity. FIG. 5 illustrates the impactof performing a task across a 10-minute Psychomotor Vigilance Task (PVT)session at two hour increments during 40 hours of total sleepdeprivation. For each PVT session, except the last one, there was animprovement from trial 10 of one PVT session to trial 1 of the next PVTsession.

[0066] A mathematical operation, shown in FIG. 1(b) as multiplication,is used to combine the results from the first, second, and third stepsinto a single predicted cognitive performance curve E in the fourthstep.

[0067] Using the preferred embodiments, predicted cognitive performanceE can theoretically reach an index level of 120, but only when cognitiveperformance capacity C is an index level of 100 (i.e., 20 minutes afterawakening from a sleep period in which cognitive performance capacity Cwas fully restored) and simultaneously the time of day function M is atits acrophase. Although possible, in practice this situation isunlikely.

[0068] The inputted data S2 into the method includes a representation ofan individual's sleep/wake history and task information. The sleep/wakehistory is a time series or temporal record based on local clock time.Each successive period, interval or epoch identifies one of two mutuallyexclusive states, namely wake or sleep. The task information is a seriesof information regarding what the individual is or is not performing inthe way of activities/tasks and alternatively the intensity, thedifficulty, the length and/or the complexity of the activity/task may beincluded in the task information. Both the sleep/wake history and thetask information are not necessarily “historical” in the sense ofoccurring in the past, but may for example be hypothetical, projected,idealized, or contemplated. The latter in particular are appropriate forthe predictive uses of this method.

[0069] The gold standard for measuring sleep and wakefulness ispolysomnography (PSG). PSG sleep scoring is based on the concurrentrecording, or at least recording in such a way as allows the lattersynchronization (typically with time-stamping or time-linking) of thedata, of electroencephalogram (EEG), electrooculogram (EOG), andelectromyogram (EMG). These signals are typically then visuallyinspected on an epoch-by-epoch basis (each epoch traditionally is 30seconds in length for PSG) to determine an individual's stage of sleepor wakefulness. Polysomnographic sleep scoring distinguishes betweenwake, non-rapid eye movement sleep (NREM) and rapid eye movement sleep(REM), with NREM sleep being further distinguished into four stages(stages 1, 2, 3, and 4) on the basis of characteristic EEG markers. PSGis not a practical method for determining sleep and wakefulness inapplied settings (e.g., while driving, working, or on the battlefield),because PSG requires that individuals be attached to sensors orelectrodes that connect with a recording device, and currently the onlyaccepted method for scoring PSG is by visual inspection of the recordedEEG, EOG, and EMG results.

[0070] Presently, if a computer is used for scoring PSG, then typicallya human reviews the results for accuracy in the scoring, becausecomputer scoring has not been approved by the American Sleep DisordersAssociation. Also, recently, researchers have been exploring whetherspectrally analyzed PSG or similar data using Fast Fourier Transformsmight provide a better measurement of sleep in humans than PSG scoring.

[0071] A preferred method of determining sleep from wakefulness would bea device that is portable, unobtrusive, reliable, and whose recordingscan be scored automatically. One such method is monitoring of movementactivity, or actigraphy. The movement activity device is typically wornon the non-preferred wrist, but may be placed elsewhere on an individual(e.g., the ankle). When worn on the non-preferred wrist, these deviceshave been shown to accurately quantify sleep and wakefulness as comparedto the standard provided by PSG (reliabilities as high as 90%).

[0072] The most widely used method of scoring actigraphy data is analgorithm developed by Cole and associates and described in theirarticle entitled “Automatic Sleep/Wake Identification from WristActigraphy” published in Sleep, vol. 15, pp. 461-469 (1992). Successfulactigraphy sleep-scoring algorithms such as the Cole et al. algorithm(also known as the Cole-Kripke algorithm) are for use with conventional(number-of-zero-crossings) actigraphs, and some algorithms account forthe number of counts above a certain threshold. These algorithms arelimited to making simple sleep vs. wake distinctions, and cannotdistinguish sleep stage changes (e.g., Stage 1 to Stage 2, or Stage 2 toREM) within sleep itself. Consequently, such algorithms cannotdiscriminate recuperative sleep (stages 2, 3, 4, and REM) fromnon-recuperative sleep (stage 1).

[0073] More recently, digital signal processing (DSP) actigraphs havebegun to be developed. Because the DSP actigraph will provide much moreinformation than just the conventional number of zero crossings orcounts above threshold (this and other information provided by aconventional actigraph will, however, be retained), it shows promise fordistinguishing between different sleep stages. Thus, sleep scoringsystems for DSP will not only replace, but will also make irrelevant,the Cole-Kripke algorithm. A sleep scoring system for the DSP will bedeveloped as the DSP database of empirical data from use of DSPactigraphs increases.

[0074] Other algorithms and methodologies for automated actigraphyscoring have been developed by, for example, Jean-Louis et al., 1996;Sadeh et al., 1989; and Zisapel et al., 1995. Each of these scoringsystems shows considerable promise, especially for scoring theactigraphically recorded sleep/wake states of individuals with sleepdisorders or other medical disorders. Available scoring systems mainlydiffer along technical aspects, for example, the extent to whichactivity counts in previous and subsequent epochs influence the scoringof the current epoch; and variation among mathematical principlesunderlying each scoring system. As one of ordinary skill in the art willrealize, any actigraph scoring system is capable of providing thesleep/wake data input for the method of this invention.

[0075] The sleep/wake history will preferably take the form of a dataseries. The sleep/wake history may include past, present, and/or future(predicted) sleep/wake patterns of an individual. The sleep/wake historyis a representation of a state of an individual as either being asleepor awake and is divided into epochs. The epochs are the same length, butthat length could be of any time period as dictated by restraints of themethod and apparatus used to collect data and/or the desired precisionof the sleep/wake pattern. The PSG or similar scoring can be convertedinto a sleep/wake history for an individual.

[0076] It can be appreciated that the accuracy of the cognitiveperformance prediction is directly related to the accuracy of thesleep/wake history input and the sleep scoring system used to interpretthe sleep/wake states of an individual. One possible source ofinaccuracy may arise from the temporal resolution of the input epoch orinterval. That is, the shorter the input epoch, the higher the temporalresolution and consequent moment-to-moment accuracy of the sleep/wakeinput. For example with actigraphy, past experience indicates that themost effective length of an epoch is one minute. Another source ofinaccuracy may arise from ambiguity in the sleep/wake discriminationitself. In the event that the history input is ambiguous (i.e., thesleep or wake state is uncertain), the calculation of predictedcognitive performance can be performed twice concurrently, once for eachpossible state (sleep or wake), resulting in a dual output representingthe possible range of expected cognitive performance. One of ordinaryskill in the art will appreciate that the dual output can be furtherdivided if there is more than one ambiguity in the sleep/wake history.Such treatment in executing the functions expressed below is included asa component of this method and any implementing apparatus.

[0077] The method of this invention is not limited with regard to timeor technique: on-line/real-time vs. off-line/post-hoc; or incremental,iterative vs. discrete solutions to continuous forms of those equations.

[0078] A preferred embodiment of the method encompasses a mathematicalmodel that expresses predicted cognitive performance capacity E at timet as a modulation of the current cognitive performance capacity C by atime of day function M by a task function T. It can be written as ageneral description in its simplest form as:

E=C∇M∇T  Equation 1

[0079] where ∇ represents a mathematical operator. Any mathematicaloperator may be used to combine cognitive performance capacity C, day oftime function M, and task function T. The form and nature of time of dayfunction M and/or task function T dictate the exact operator that ismost desirable. There may be two different operators used to express thepredicted cognitive performance capacity E such that the first ∇ may beone mathematical operator and the second ∇ may be a second mathematicaloperator. Alternatively, the modulations could be performed in two stepswhere two of the items are modulated with the resulting modulated valuebeing modulated with the third of the items. Most preferably, Equation1a below would be used to combine cognitive performance capacity C, dayof time function M, and task function T.

E=C*M*T  Equation 1a

[0080] In the alternative, Equation 1b below could also be used tocombine cognitive performance capacity C, time of day function M, andtask function T.

E=C+M+T  Equation 1b

[0081] Cognitive performance capacity C represents a function ofsleep/wake history, that is

C=w(t)+s(t)+d(t)+i(t)  Equation 2

[0082] where w(t), s(t), d(t), and i(t) are the instantaneous values ofthe wake, sleep, delay, and sleep inertia functions at time t. Time ofday function M represents a function of the time of day, such that

M=m(t)  Equation 3

[0083] where m(t) is the instantaneous value of the time of day functionat time t. Task function T represents a function of the impact ofperforming or not performing a task when the individual is awake, suchthat

T=t(t)  Equation 4

[0084] In keeping with the invention, a four-step process may beperformed after either an initial setting of the starting time t, thestarting cognitive performance capacity C, and the time of the lasttransition t_(LS) when appropriate in S1 of FIG. 4(a) where these datacan be entered in any order. In the first step, the level of cognitiveperformance capacity C at time t may be calculated based on anindividual's sleep/wake history using functions w(t), s(t), d(t), andi(t) as represented by S3-S7e in FIGS. 4(a)-(b). In the second step,time of day modulator M may be calculated using the time of day functionas represented by S8 in FIG. 4(b). According to an aspect of theinvention, the second step can be performed once to provide a series ofdata points in time sequential order for multiple executions of thefirst step. In the third step, task modulator T may be calculated usingthe task function as represented by S9 a through S10 c in FIG. 4(b). Inthe fourth step, predicted cognitive performance E may be derived fromthe combination of cognitive performance capacity C and time of daymodulator M resulting in cognitive performance capacity C beingmodulated by time of day modulator M being modulated by task modulator Tas illustrated by S11 in FIG. 4(b).

[0085] First Step: Calculation (or Determination) of CognitivePerformance Capacity C

[0086]FIG. 2 is a schematic flow diagram representing the use of thefunctions described below. Examples of the calculations discussed aregraphically illustrated in FIGS. 3(a)-(c). FIGS. 4(a)-(b) are a detailedflowchart of the steps in the method. As a preferred embodiment of themodel, cognitive performance capacity C is herein assigned index valuespreferably having a total range of zero to 120. The ranges in thisapplication are intended to encompass the end points of the statednumerical range. However, cognitive performance capacity C may be scaledto other values or units for specific applications, for example, zero to100.

[0087] In the preferred embodiment, only one of the four functions w(t),s(t), d(t), and i(t) operates at any given interval of time, and theothers are equivalent to zero in Equation 2 as represented by S7 athrough S7 d. Functions w(t) and s(t) describe the non-transitionstates, while functions d(t) and i(t) describe the transition states.For instance in a non-transition state, when the individual is awake,function s(t) is set to zero, and when the individual is asleep,function w(t) is set to zero. Likewise, during specific intervals oftransition from wake to sleep and vice versa, only one of the transitionfunctions d(t) or i(t) operates, the other being set equal to zero. Whenthere is a change between sleep and wake, or vice versa, a time countert_(LS) is reset to keep track of the time in the present state fordetermining decision rules for the transition functions d(t) and i(t) asshown in FIG. 4(b).

[0088] (1) Wake function (w(t))

[0089] The wake function S7 a represents the depletion of cognitiveperformance capacity with the passage of time awake. It is based onevidence that (1) near-100% cognitive performance is maintained from dayto day when individuals obtain eight hours of sleep each night; and (2)cognitive performance appears to decline by approximately 25% for every24 hours of wakefulness.

[0090] In S7 a, the wake function w(t) calculates the current value ofcognitive performance capacity C resulting from the decay in cognitiveperformance capacity that occurs over an interval of time from t−1 to t,which in the preferred embodiment is the length of one epoch. As notedabove, this calculation is performed independent of and prior tomodulation of cognitive performance capacity C by the time of dayfunction M in S9. A generalized form of the wake function is given bythe equation:

C_(w) =w(t)  Equation 5

[0091] where wake function w(t) may be any positive-valued functiondecreasing with t. More preferably, the wake function w(t) is a linearfunction depleting performance at a constant rate, and, most preferably,the wake function w(t) is expressed at time t as follows:

w(t)=C _(t−1) −k _(w)  Equation 5a

[0092] where the interval of wakefulness is from t−1 to t (in epochs)and the decay in performance per minute is k_(w). Thus, if t−1 to t isnot one minute, then k_(w) is appropriately adjusted. The total range ofk_(w) is any positive real number, and preferably k_(w) is a range of0.003 to 0.03 of a point per minute, and most preferably k_(w) is equalto approximately 1 point per hour or 0.017 of a point per minute. Thevalue k_(w) is based on empirical data showing that cognitiveperformance declines by approximately 25 points for every 24 hours ofcontinuous wakefulness. Equation 4a is represented in FIGS. 2 and 4(b)at S7 a. An example is illustrated as the wake function in FIG. 3(a),for an initial cognitive performance capacity of 100 index points, adecay rate of 0.017 of a point per minute, over an interval of 16 hours(960 minutes).

[0093] (2) Sleep function (s(t))

[0094] The sleep function S7 c restores cognitive performance capacitywith the passage of time asleep. The sleep function s(t) is based onempirical evidence that the recuperative value of sleep on cognitiveperformance accumulates in a nonlinear manner. That is, the rate ofcognitive performance capacity recuperation is higher initially duringsleep and slows as the time asleep accumulates. Other data indicatesthat sleep beyond a certain point confers little or no additionalbenefit for cognitive performance and the rate of recuperationapproaches zero. Thus, for example, two hours of sleep are not twice asrecuperative as one hour of sleep. The sleep function increasescognitive performance capacity at a rate that depends on the currentlevel of cognitive performance capacity—the lower the initial cognitiveperformance capacity, the more rapidly recuperation accumulates. Inother words, preferably the slope of a tangential line for a particularcognitive performance capacity index level is the same each time thatindex level is reached during different sleep periods.

[0095] For example, following a full day (16 hours) of wakefulness,during ensuing nighttime sleep recuperation accumulates rapidly early inthe night. As cognitive performance capacity is restored across thesleep period, the rate of recuperation declines. Following sleepdeprivation, initial cognitive performance capacity is even lower thanit would be following a normal 16-hour day, and the rate of recuperationis even higher than at the beginning of recovery sleep. During chronicpartial sleep deprivation, cognitive performance capacity may not becompletely restored each night despite this more rapid initialrecuperation rate.

[0096] The sleep function calculates the current value of cognitiveperformance capacity C resulting from the recovery of capacity thatoccurs while an individual is asleep over an interval of time T (fromt−1 to t). As noted above, this calculation is performed independent of,and prior to, modulation of C by the time of day function M andmodulation by the task function T. A generalized form of the sleepfunction is given by the equation:

C _(s) =s(t)  Equation 6

[0097] where sleep function s(t) may be any positive-valued functionincreasing with t, and more preferably the sleep function s(t) is anexponential function. This is based on empirical data showing thatcognitive performance restoration during sleep is nonlinear, with therate of recuperation highest initially and gradually slowing as sleepcontinues. Thus, the most preferred sleep function is an exponentialfunction, which in its discrete form is stated as:

C _(t) =C _(t−1)+(100−C _(t−1))/k _(s)  Equation 6a

[0098] where the interval of sleep is from t−1 to t (in minutes), themaximum cognitive performance capacity value is 100 index points,C_(t−1) is cognitive performance capacity in the period preceding timet, and k_(s) is the recuperation “time constant”. In other words, k_(s)is the time required to fully restore cognitive performance capacity Cif it was restored at a constant rate equal to the initial slope of thecurve. The recuperation time constant k_(s) is derived empirically frompartial sleep deprivation data and is selected based on the length ofthe epoch. In accordance with the preferred embodiment, k_(s) is equalto any positive real number. For example, k_(s) may be in the range of100 to 1000 with an epoch length of one minute, and, more particularlymay be approximately 300 with an epoch length of one minute. However,the optimum values for k_(s) will depend at least in part on the lengthof the epoch. Equation 6a is represented in FIGS. 2 and 4(b) as S7 c. Agraphical example is illustrated as the sleep function in FIG. 3(a),using an initial cognitive performance capacity level of 100 indexpoints, and using a time period of one minute and k_(s)=300, the effectof eight hours of sleep following 16 hours of wakefulness.

[0099] (3) Delay function d(t) for wake to sleep transitions

[0100] The delay of recuperation function d(t) defines the duration ofan interval after sleep onset during which recuperation of cognitiveperformance capacity from the sleep function is delayed. During thisinterval, the wake function degradation of cognitive performancecapacity continues as represented by S7 d in FIG. 4(b). By preventingimmediate accumulation of cognitive performance capacity at thebeginning of a sleep period or following awakenings from sleep, thisdelay adjusts the cognitive performance capacity calculation S6 b.

[0101] The delay of recuperation function is based upon empiricalstudies showing that the first few minutes of sleep are generallycomprised of stage 1 sleep, which is not recuperative for sustainingcognitive performance capacity. Frequent arousals to wake or stage 1sleep (sleep fragmentation) drastically reduce the recuperative value ofsleep on cognitive performance capacity. Available data suggest thatfive minutes is the approximate length of time required to return torecuperative sleep (stage 2 or deeper sleep) following an arousal towake or stage 1 sleep. If many hours of sleep are obtained withoutinterruption, then the delays make only a small difference in overallrestoration of cognitive performance capacity. If sleep is interruptedwith frequent awakenings, the delays in recuperation after eachawakening will accumulate, and thus substantially reduce total cognitiveperformance capacity restored during the total sleep period.

[0102] The delay function specifies the duration of a sleep intervalduring which application of the sleep function is postponed and atransitional formula is applied. A generalized form of the delayfunction for wake to sleep transitions is expressed as a decision rule:

d(t): IF (t−t _(LS))≦k _(d)

THEN C _(t) =d(t)

ELSE C _(t) =s(t)  Equation 7

[0103] where LS stands for last state change, and thus the wake to sleeptransition time t_(LS) denotes the time of the last wake intervalpreceding a contiguous series of sleep intervals. This decision rule isshown in FIGS. 2 and 4(b) as S6 b, S7 c and S7 d taken together. Forcalculating cognitive performance capacity during the interval k_(d),cognitive performance capacity C_(t) is evaluated by a transitionalformula C_(t)=d(t). After k_(d) has elapsed, C_(t)=s(t). Note that ifwakefulness ensues before the end of k_(d), then C_(t) never reverts tos(t). That is the sleep function is not applied during the brief sleepinterval.

[0104] It is believed that the preferred range for k_(d) is from 0 to 30minutes, more preferably k_(d) equals about five minutes measured fromthe time of sleep onset before recuperation is derived from sleep.Preferably d(t) equals w(t). One of ordinary skill in the art willrealize there are a variety of factors that influence the length ofk_(d). Thus a more preferred delay function may be expressed as:

d(t): IF (t−t _(LS))≦5

THEN C_(t) =w(t)

ELSE C_(t) =s(t)  Equation 7a

[0105] The effects of delayed recovery on cognitive performancecapacity, as embodied by Equation 7 a, are graphically illustrated indetail in FIG. 3(b).

[0106] As one of ordinary skill in the art will appreciate, PSG orsimilar scoring is able to classify when stage 1 sleep occurs. Theconversion of PSG or similar scoring data would then convert theoccurrences of stage 1 sleep into wake data for the sleep/wake history.Consequently, when the sleep/wake history is based on converted PSG orsimilar scoring data, the delay function d(t) is not necessary for thedetermination of an individual's cognitive performance capacity.Alternatively, the delay function could be determined based upon whenthe individual entered stage 2 or deeper sleep instead of using thek_(d) value, and that once stage 2 or deeper sleep is reached then thesleep function s(t) would be used.

[0107] Alternatively, the delay function d(t) may simply maintain thecognitive level of C_(t) at the beginning of the delay period, i.e.,CtLS.

[0108] (4) Sleep inertia function i(t) for sleep to wake transitions

[0109] The sleep inertia function i(t) defines the duration of aninterval after awakening from sleep during which manifest cognitiveperformance capacity is suppressed below the actual current level. Thesleep inertia function i(t) is based upon empirical data showing thatcognitive performance is impaired immediately upon awakening, butimproves primarily as a function of time awake. It is also based onpositron emission tomography studies showing deactivated heteromodalassociation cortices (those areas that mediate this cognitiveperformance) immediately upon awakening from sleep, followed byreactivation of these areas over the ensuing minutes of wakefulness.That is, actual cognitive performance recuperation realized during sleepis not apparent immediately after awakening. The data indicate that 20minutes is the approximate length of time required for cognitiveperformance capacity to return to levels that reflect actualrecuperation accrued during sleep.

[0110] A sleep inertia delay value k_(i) specifies the duration of theinterval after awakening during which manifest cognitive performancecapacity may be transitionally suppressed below the sleep-restoredcognitive performance capacity level. During this interval, atransitional function bridges from an initial level to that determinedby the wake function alone. A generalized form of the sleep inertiafunction for sleep to wake transitions is expressed as a decision rule:

i(t): IF (t−t _(LS))<k _(i)

THEN C _(t) =i(t)

ELSE C _(t) =w(t)  Equation 8

[0111] where the sleep to wake transition time t_(LS) denotes the timeof the last sleep interval preceding a contiguous series of wakeintervals. For calculating cognitive performance capacity during theinterval k_(i), C_(t) is evaluated by a transitional formula C_(t)=i(t).After k_(i) has elapsed, C_(t)=w(t). Equation 8 is represented in FIGS.2 and 4(b) as S6 a, S7 a and S7 b taken together.

[0112] The preferred range for k_(i) is from 0 to about 60 minutes, andpreferably in the range of about 10 to about 25 minutes, and mostpreferably between 18 and 22 minutes.

[0113] The sleep inertia function i(t) may be any function over theinterval 0 to k_(i), preferably any negatively accelerated function. Apreferred sleep inertia function i(t) is a simple quadratic equation.This function preferably suppresses cognitive performance capacity by10% to 25% immediately upon awakening, and most preferably by 25%. Thefunction recovers 75% of the suppressed cognitive performance capacityin the first 10 (or about half of k_(i)) minutes after awakening and100% of the suppressed cognitive performance capacity usually by 20minutes after awakening, after which the wake function resumes. Thesevalues are based on empirical data concerning the transition from sleepto wake. These studies show that cognitive performance is impairedimmediately upon awakening from sleep, that the bulk of this impairmentdissipates within the first few minutes of awakening, and thatapproximately 20 minutes is required for performance to be fullyrestored. Using the preferred 25% suppression of cognitive performancecapacity and 20 minute recovery time, the preferred form of the sleepinertia function is expressed as a decision rule:

i(t): IF (t−t _(LS))<20

THEN C _(t) =C _(sw)* [0.75+0.025 (t−t _(LS))−(0.025 (t−t _(LS)))²]

ELSE C _(t) =w(t)  Equation 8a

[0114] where C_(sw) is cognitive performance capacity at the end of thesleep period calculated by the sleep function at the sleep to waketransition time t_(LS). This decision rule is shown in FIGS. 2 and 4(b)as S6 a, S7 a, and S7 b taken together. Equation 8a illustrates aninitial suppression of 25% and k_(i) equal to 20 minutes, and anegatively accelerated ramp bridging the interval until the wakefunction w(t) resumes its effects. The effect of the sleep inertiafunction i(t) on cognitive performance capacity, as embodied by Equation8 a, is graphically illustrated in FIG. 3(c). An alternative variant ofthe sleep inertia function i(t) is a linear equation based on k_(i)equal to 10 minutes and an initial 10% decrease in cognitive performancecapacity. The resulting decision rule is then:

i(t): IF (t−t _(LS))<10

THEN C _(t) =C _(sw)* [0.9+(t−t _(LS))/100]

ELSE C _(t) =w(t)  Equation 8b

[0115] As one of ordinary skill in the art will realize, both Equations8a and 8b can be adjusted for a change in the value of k_(i) and amountof the initial suppression of cognitive performance capacity.

[0116] Second Step: Calculation of the time of day modifier M

[0117] (1) Time of day function m(t)

[0118] The time of day function m(t) shown at S8 in FIG. 4(b) describesthe cyclical 24-hour variation in cognitive performance. The time of dayfunction m(t) is based on empirical data showing that under constantroutine and/or total sleep deprivation conditions (i.e., with sleep/wakehistory controlled), cognitive performance capability oscillates betweenapproximately 5% to approximately 20% peak to peak over a 24-hourperiod. This effect is commonly attributed to circadian rhythms ofindividuals. Output from this function modulates the current cognitiveperformance capacity prediction C (calculated in the first step)according to the time of day. The result of this modulation is thepredicted cognitive performance capacity E. A generalized form of thetime of day function is given by

M=m(t)  Equation 9

[0119] where m(t) can be any rhythmic function with a base period of 24hours, and, preferably, m(t) is the sum of two sinusoids, one with aperiod of 24 hours and the second with a period of 12 hours, whichprovides a biphasic circadian component. This function may be based onempirical data showing that a considerable proportion of variabilityseen in cognitive performance measurements can be accounted for by twosuch sinusoidal waveforms. As previously noted, the peak in empiricallyobserved cognitive performance capacity occurs usually between 8:00 PMand 10:00 PM, and the trough occurs usually between 2:00 AM and 6:00 AM,providing a variation of approximately 5% to approximately 20% each day.A secondary trough occurs usually around 3:00 PM. Using these values forthe preferred form of function m(t), the resulting function accounts forthe empirically demonstrated asymmetry of daily cognitive performancerhythms, with a mid-afternoon decrease.

[0120] The descriptive form of the function m(t), including its offsetand amplitude values varies with the operator selected for the thirdstep. The computed value of the function can be expressed either as anadditive percentage of cognitive capacity (dependent or independent ofthe current value of cognitive performance capacity C_(t)) or as amultiplicative dimensionless scalar. The preferred form of the function,using the multiplicative operator, is expressed as

m(t)=F+(A ₁ * cos (2Π(t−V ₁)/P ₁)+A ₂ * cos (2Π(t−V ₂)/P ₂))  Equaton 9a

[0121] where F is an offset, t is the time of day, P₁ and P₂ are periodsof two sinusoids, V₁ and V₂ are the peak times of day in time units orepochs past midnight, and A₁ and A₂ are amplitudes of their respectivecosine curves. This function may be used to modulate the previouslycalculated cognitive performance capacity C. Equation 9 a is shown as S8in FIGS. 1(a) and 4(b) and graphically illustrated as G8 in FIG. 1(b).As shown in FIG. 4(b), t is an input in the time of day function m(t)for each epoch of data.

[0122] For example in a preferred embodiment the variables are set asfollows: t is the number of minutes past midnight, P₁ is equal to 1440minutes, P₂ is equal to 720 minutes, V₁ is equal to 1225, and V₂ isequal to 560. Further, when A₁ and A₂ are represented as scalars, theiramplitudes are in a range from 0 to 1, and more preferably are in arange from 0.01 to 0.2, and most preferably A₁ is equal to 0.082 and A₂is equal to 0.036. Further in this example F is equal to either 0 or 1,and more preferably F is equal to 1. The resulting value of the time ofday function m(t), in this example, is in the range of 0 to 2, andpreferably in the range of 0.8 to 1.2, and most preferably in the rangeof 0.92 to 1.12.

[0123] As mentioned above, the second step may, for example, bepreformed on the fly, for example, in real time or be previouslycalculated prior to the first step.

[0124] Third Step: Calculation of the Time on Task Modulator T

[0125] In the preferred embodiment, only one of the two functions g(t)and h(t) operates during any period in which the individual is awakewith the other function being equivalent to zero. However, when theindividual is asleep then both functions g(t) and h(t) are equal to zeroas represented by S9 a through S10 c in FIG. 4(b). The selection of thefunction preferably is based upon whether the individual is performing atask or not is illustrated by S9 b through S10 b and the individual isawake is illustrated by S9 a and S10 c. As such, the time on taskmodulator may be calculated prior to (as illustrated in FIG. 8(a)) orafter steps S7 a and S7 b instead as a separate branch as illustrated inFIG. 4(b).

[0126] (1) Rest function g(t)

[0127] The rest function g(t) is illustrated as S10 a in FIG. 4(b). Therest function g(t) preferably represents the restoration of cognitiveperformance capacity due to an individual resting and relaxing betweentasks and/or activities. The rest function g(t) preferably does notprovide the same amount of restoration that occurs during sleep asdiscussed above with respect to the sleep function s(t). A generalizedform of the rest function is given by the equation:

t(t)=g(t)  Equation 10

[0128] where g(t) may be any positive-valued function. Alternatively,the rest function g(t) may be expressed as follows:

g(t)=z * s(t)  Equation 10a

[0129] where z is a scalar, which preferably is in a range of 0 to 1,and t_(LS) will preferably represent the length of the resting and/orinactivity period.

[0130] (2) Work function h(t)

[0131] The work function h(t) is illustrated as S10 b in FIG. 4(b). Thework function h(t) preferably represents declination of cognitiveperformance capacity due to an individual performing a task(s) and/or anactivity(ies). In S10 b, the work function h(t) calculates the taskmodulator T resulting from the decay in cognitive performance capacitythat occurs over an interval of time from t−1 to t, which in thepreferred embodiment is the length of one epoch. A generalized form ofthe work function is given by the equation:

t(t)=h(t)  Equation 11

[0132] where h(t) may be any negative-valued function decreasing with t.More preferably, the work function h(t) is a linear function depletingperformance at a constant rate. Alternatively, the work function h(t)may be an exponential function that, for example, may increase thedepletion rate the longer the task is performed and/or activity is done.Another or further alternative is that the type of task, i.e., thedifficulty, the complexity, and/or the intensity will impact thedepletion rate per epoch. The greater the difficulty, the complexity,and/or the intensity of the task, then the greater the depletion rateper epoch will be.

[0133] Alternatively, both or just one of the rest function and the workfunction may be impacted by the time of day modulator M such that priorto being modulated with the cognitive performance capacity C and thetime of day modulator M, the task modulator T is modulated based uponthe time of day as represented by the time of day modulator M. A furtheralternative is for the time of day modulator M to be used twice inEquations 1, 1a, and 1b above.

[0134] (3) Asleep Function

[0135] A generalized form of the task function when the individual isasleep is

t(t)=1  Equation 12

[0136] where the modulation is performed using multiplication, becausethe task function T will not impact the individual's cognitiveperformance index. Alternatively, if the task modulator is added to theother functions, then the task function will take the following form

t(t)=0  Equation 12a

[0137] Fourth Step:Calculation of Predicted Cognitive Performance

[0138] The overall process of calculating predicted cognitiveperformance capacity E is illustrated schematically in FIGS. 1(a) and4(a)-(b). The time of day function M and the task function T modulatethe cognitive performance capacity C derived from the individual'ssleep/wake history to generate the final predicted cognitive performanceE as shown in, for example, FIG. 1(a). In the third step, predictedcognitive performance E is derived from the combination of cognitiveperformance capacity C, time of day function M, and task function T. Inits most general form:

E=C∇M∇T  Equation 1

[0139] where ∇ is any mathematical operation for combining cognitiveperformance capacity C, time of day function M, and task function T. Theconventional choice of operations for providing this combination isaddition or multiplication. Depending on the form of time of dayfunction m(t) and task function T(t) selected above, the same numericalvalue of predicted cognitive performance E can be generated by eitheroperation. Most preferably the combination is performed withmultiplication S11, represented as:

E=C*M*T  Equation 1a

[0140] In Equation 1a, the predicted cognitive performance E is themodulation of the current cognitive performance capacity C with a valuecentered around the number one representing the current value of thetime of day modulator M and the task modulator T.

[0141] As noted above, the preferred numerical representation ofcognitive performance capacity C is a value ranging from zero to 100 torepresent an index (or a percentage) of cognitive performance capacityavailable for a particular individual. However, predicted cognitivecapacity E can meaningfully exceed 100 under certain circumstances dueto time of day modulation about the current value of cognitiveperformance capacity C. A possible example of such a circumstance wouldbe a sleep period resulting in an index level of 100 cognitiveperformance capacity C and terminated at the evening peak (and aftersleep inertia has dissipated).

[0142] Alternatively, if a percentage representation is used whileretaining a 100% scale, either the predicted cognitive capacity E may betruncated/clipped at 100% or 0 to 120% may be scaled to 0 to 100%.Either choice will maintain a maximum of 100%. This most likely will beimplemented as scaling 120% to 100% and then truncating/clipping anypredicted cognitive capacity E to 100% if the prescaled value exceeds120%.

[0143] As shown in FIG. 1, the method repeats for each new epoch ofdata. For each iteration of the method, one time unit equal to thelength of an epoch may be added to time t preferably in the form of acounter S13 as exemplified in FIG. 4(b). The counter step S13 may occur,for example, as illustrated in FIG. 4(b), at the same time as S11 orS12, or after S12.

[0144] In the preferred embodiment described above, the sleep inertiafunction i(t) is applied to cognitive performance capacity C prior tomodulation of cognitive performance capacity C by the time of daymodulator M and/or task modulator T. An alternative embodiment appliesthe sleep inertia function i(t) not to cognitive performance capacity C,but to predicted cognitive capacity E, that is, subsequent to themodulation of cognitive performance capacity C by time of day modulatorM and/or task modulator T.

[0145] Also in the preferred embodiment described above, the wakefunction w(t) is set to zero when the sleep inertia function i(t) isapplied. Another alternative embodiment applies the sleep inertiafunction i(t) and the wake function w(t) simultaneously. When the sleepinertia function i(t) and the wake function w(t) become equal to eachother or the sleep inertia function i(t) becomes greater than the wakefunction w(t), then cognitive performance capacity C is calculated (ordetermined) using the wake function w(t).

[0146] The preferred embodiment may be further modified to account forthe effects of narcotics or other influences that will impact thecognitive capacity as shown in FIG. 6. Further modification to thepreferred embodiment will allow for the inclusion of jet lag and similartime shifting events by, for example, compressing or expanding the 24hour period of the time of day function M(t) over a period of days torealigning the time of day function M(t) to the adjusted schedule.

[0147] The preferred embodiment may be modified to include the testingof the individual at regular intervals to collect additional data andadjust the current cognitive performance index to reflect the results ofthe test. A test that may be used is the PVT or similar reaction timemeasurement test(s). The current cognitive performance index at the timeof the test then is adjusted preferably along with the underlyingweights of variables discussed above in connection to the Equations suchthat the method and/or apparatus is fine-tuned to reflect a particularindividual's recuperation and/or depletion of cognitive performancecapacity.

[0148] Another alternative embodiment is the removal of the third stepfrom the preferred embodiment. Like the other alternative embodiments,this alternative embodiment may be combined in a variety of ways withthe other alternative embodiments.

IMPLEMENTATION OF THE METHOD

[0149] The preferred embodiment may be realized as software to provide arealtime current state of an individual's cognitive performance and thecapability upon demand to extrapolate future levels of cognitiveperformance. A flowchart representing the steps to be performed by thesoftware in the preferred embodiment is shown in FIGS. 4(a)-(b) and foran alternative embodiment, to be described later, in FIGS. 8(a)-(b).

[0150] The software may be implemented as a computer program or otherelectronic device control program or an operating system. The softwareis preferably resident in a device, e.g. an actigraph, attached to theindividual or in a stand-alone device such as a personal computer, a PALdevice, a personal digital assistant (PDA), an e-book or other handheldor wearable computing devices (incorporating Palm OS, Windows CE, EPOC,or future generations like code-named products Razor from 3Com orBluetooth from a consortium including IBM and Intel), a specific purposedevice receiving signals from a device, e.g. an actigraph, attached toan individual or human input from human analysis or observation. Thesoftware could be stored, for example, in random access memory (RAM); inread only memory (ROM); on a storage device like a hard drive, disk,compact disc, punch card, tape or other computer readable material; invirtual memory on a network, a computer, an intranet, the Internet, theAbilene Project, or otherwise; on an optical storage device; on amagnetic storage device; and/or on an EPROM. The software may allow forthe variables in the equations discussed above to be adjusted and/orchanged. This capability will allow users to adjust the variables basedon empirical knowledge and also learn the interrelationship between thevariables.

[0151] The software implementation onto the measuring device such as anactigraph will convert any decimal numbers used in calculations intointegers that are appropriately scaled as is well known to those skilledin the art. Further the integers would then be approximated such thatminimal error would be created, for example, approximation for theCole-Kripke algorithm weighting factors become 256, 128, 128, 128, 512,128, and 128, respectively. Using linear approximation will simplify thebinary arithmetic and the corresponding assembly code for softwareimplementation.

[0152] In software, the time of day modulator would be embodied as atable with one hour steps resulting in 24 rows using 8-bit unsignedintegers. The intervening steps would be interpolated from the one hoursteps to provide 15-minute steps. This simplification providessufficient resolution for available displays. A pointer system would beutilized to retrieve the appropriate data to calculate the time of daymodulator. Depending on a myriad of factors, one of ordinary skill inthe art will most likely choose a multiplicative modulation to achieveappropriate scaling or an additive modulation for less complex but morerapid evaluation, i.e., if speed is a concern. The main disadvantagewith the additive modulation is that there will be an approximately 3%error compared to the 1% error using the multiplicative modulation inthis invention. This system will allow the time of day function to beuploaded when the measuring/recording device, such as an actigraph, isinitialized and reduce the repetitive computational burden that wouldexist if a cosine table was used and the time of day function wascalculated from the cosine table for each epoch.

[0153] The preferred embodiment, as shown in FIG. 7(a), may also berealized by a stand-alone device or a component add-on to a recordingdevice. The stand-alone device is separate from the device or othermeans of recording an individual's sleep history. In contrast, thecomponent add-on to a recording device includes modifying the recordingdevice to include the component add-on to provide one device that bothrecords and analyzes an individual's sleep history.

[0154] A suitable stand-alone device includes a physical inputconnection, e.g., an input port (input means 20) to be physicallyconnected to an input device, e.g., a keyboard, data entry device, or adata gathering device such as an actigraph. Alternatively, the physicalconnection may occur over an information network. Alternatively, theinput port may be an interface to interact with a user. Alternatively,the physical input connection may be realized by a wirelesscommunication system including telemetry, radio wave, infrared, PCS,digital, cellular, light based system, or other similar systems. Thewireless communication system has an advantage in that it eliminates theneed for a physical connection like cables/wires, plug-ins, etc. whichis particularly convenient when monitoring a mobile subject. The datagathering or data entry device provides a sleep history that may includepast, present and/or predicted/anticipated sleep patterns of anindividual. Input means 20 embodies S1 for initial inputting ofinformation and S2 for the continual or one-time loading of datadepending upon the implementation selected.

[0155] The stand-alone device further includes a data analyzer(interpretation means 30). The data analyzer performs S3-S6 b.Interpretation means 30 analyzes the input data by performing differentanalysis functions. Interpretation means 30 compares the present inputdata to the last input data to determine if there has been a change fromsleep to wake or wake to sleep; and if so, then set a time counter tothe time for the last state, S3 and S4 a in FIG. 4(a). Interpretationmeans 30 also classifies the inputted data, as represented by S5 in FIG.4, to then be able to select or generate at least one of the followingcalculation functions responsive to the composition of the inputdata: 1) wake function, 2) sleep function, 3) delay function, and 4)sleep inertia function as depicted by S6 a-S7 d in FIG. 4(b).Interpretation means 30 may be realized by an appropriately programmedintegrated circuit (IC). One of ordinary skill in the art will realizethat a variety of devices may operate in concert with or be substitutedfor an IC like a discrete analog circuit, a hybrid analog/IC or othersimilar processing elements.

[0156] The stand-alone device further includes a calculator(determination means 40). Determination means 40 may be implemented byappropriately programming the IC of the interpretation means or it maybe implemented through a separate programmed IC. Determination means 40calculates the cognitive performance capacity factoring in thesleep/wake history and the current state using the function selected byinterpretation means 30, S7 a-S7 d in FIG. 4(b).

[0157] The interpretation means 30 and determination means 40 may becombined into one combined means or apparatus.

[0158] The stand-alone device further includes a first memory 60 thatstores modulation data including a modulating data series or curvepreferably representing a time of day curve. The stand-alone devicefurther includes a second memory 50 that holds data for the creation ofa data series or a curve representing cognitive performance capacity Cover time t. The first memory 60 and the second memory 50 may be anymemory or storage method known to those of ordinary skill in the art.The second memory 50 is preferably a first-in-first-out memory providingmeans for adding the value from the determination means 40 to the end ofthe data series or the curve. The first memory and the second memory maybe combined as one memory unit. As one of ordinary skill in the art willrealize there may be a memory to store the various intermediary valuesnecessary for calculating cognitive performance capacity C and predictedcognitive performance E as required to implement this invention aseither hardware or software.

[0159] The stand-alone device also includes, as a separate IC or incombination with one of the previously mentioned ICs, a modulator(modulation means 70) embodying S8-S9 shown in FIG. 4(b). Modulationmeans 70 receives the present cognitive performance capacity calculatedby determination means 40 and calculates the time of day value from datastored in the first memory 60. Modulation means 70 modulates the firstdata series or curve (cognitive performance capacity) with the time ofday value. The modulation preferably is performed by matching the timingsequence information relating to the data series or the curves based onthe latter of midnight and the length of time from the initial input ofdata as preferably determined by the number of epochs and the initialstarting time related to the first entered sleep/wake state. Modulationmeans 70 may modulate a series of cognitive performance capacity valueswith the time of day function if the second memory 50 exists to storethe cognitive performance capacity values.

[0160] As is well known by one of ordinary skill in the art, a counteror other similar functioning device and/or software coding may be usedin the stand-alone device to implement S11 shown in FIG. 4(b).

[0161] The stand-alone device may also include a display to show aplotted modulated curve representing the modulation result over time, asstored in a memory, e.g. a first-in-first-out memory, or a numericalrepresentation of a point on the modulated curve at a selected time fromthe modulation means 70 representing the predicted cognitive performanceE. The numerical representation may take the form of a gauge similar toa fuel gauge in a motor vehicle or the number itself. The stand-alonedevice, as an alternative or in addition to the display, may include aprinter or communication port to communicate with an external device forprinting and/or storage of a representation of predicted cognitiveperformance E.

[0162] The stand-alone device instead of having dedicated hardware mayprovide the storage space and processing power to execute a softwareprogram and accompanying data files. In this case, the stand-alonedevice may be a desktop computer, a notebook computer, or similarcomputing device. The software program handles the receiving of the datarepresenting sleep history from an outside source through acommunication port or via a computer network such as intranets and theInternet, and then performs the necessary analysis and processing of themethod described herein. The storage space may be a memory in the formof computer readable material storing at least the time of day curve andpossibly the input data, which may also be resident in therandom-access-memory (RAM) of the computer given its temporary use. Theinput data and the resulting produced data indicating various cognitiveperformance levels of an individual may also be saved to a morepermanent memory or storage than is available in RAM.

[0163] An alternative embodiment modifies the input port 20 to receivesome form of raw data, i.e., prior to being sleep scored, representingsleep activity of an individual. In this embodiment, the interpretationmeans 30 would then sleep score the raw data as part of the dataanalysis performed by it. A third memory to store the weighting factorsrequired for sleep scoring, if a table is used for them, else the sleepscoring function will implicitly include the weighting factors and thethird memory will be unnecessary.

[0164] Another alternative embodiment provides for the interpretationmeans 30 to filter the sleep/wake data such that for the first k_(d)number of sleep epochs after a wake epoch are changed to wake epochs. Inkeeping with the invention, the filtering may be accomplished a varietyof ways. The preferred way is to add a decision step prior to S3 in FIG.4(a) such that if D_(sw) is a sleep epoch and t−t_(LS)≦k_(d), then S3-S6a will be skipped and S7 a will occur. The result is that the decisionrule represented as d(t) in Equation 6 above would be eliminated, and S6b and S7 d would be unnecessary in FIGS. 4(b) and 8(b).

[0165] One of ordinary skill in the art will appreciate that thestand-alone device is broad enough to cover a computer/workstationconnected to the Internet or other computer network. A user wouldtransmit their sleep/wake history over such network to the stand-alonedevice for obtaining a predicted cognitive performance based on thetransmitted data. The interface of the stand-alone device may allow theuser to adjust the variables discussed above in connection with themethod to learn the interrelationship between the variables and thepredicted cognitive performance. Preferably, the range of allowableadjustment of the variables would be that of the respective rangesdiscussed in connection with each of the variables above.

[0166] The component add-on to the measuring device may have similarcomponents to the stand-alone device described above and shown in FIG.7(a). Preferably the component add-on is contained in one integratedchip to minimize the space needed to house it and/or is implemented assoftware as part of a designed measuring device. The input means 20becoming, for example, a wire or other type of connector. However, theadd-on component may include more than one electrical component, e.g., amemory chip and an IC. The component add-on may transmit the predictedcognitive performance to a remote device for further analysis.

[0167] The apparatus for accomplishing the third step is illustrated aspart of FIG. 7(b). The additional components preferably include a taskinput means 20′ for receiving information regarding the task that mayeither be manually provided through some sort of data entry mechanismsuch as a keyboard, touch pad, a button or set of buttons, a touchscreen or other similar mechanisms, or through analysis of the datacollected by the attached device. Alternatively, the task input means20′ may be a part of or similar to the input means 20. Preferably, adetermination means 40′ for calculating the task modulator based on whatis received from the task input means 20′. The determination means 40′preferably is in communication with the modulation means 70′, which isthe modulation means 70 with the added modulation of the task modulator.As with the device described in connection with FIG. 7(a), the variouscomponents of FIG. 7(b) may be consolidated into one or a series ofcombination components. Additionally, the components in FIGS. 7(a) and7(b) may also not be directly connected but separated into differentdevices.

[0168] The alternative embodiment described above involving real-timeadaptation of the above-described method may be implemented by theaddition of a routine such that the individual is notified to press abutton for recalibration on the recording device. Based upon theindividual's response time, the individual's current level of cognitiveperformance is determined and adjustments are accordingly made to theabovedescribed Equations to allow for the recent activity of theindividual to lead to the determined cognitive performance.

[0169] Both the software and/or hardware are envisioned as being able tooperate and function in real-time. For the purposes of this invention,real-time is understood to represent a continuous stream and analysis ofcognitive performance level for each epoch of sleep/wake data entered.Thus, the software and/or hardware will both provide to an individual orsome other entity the present cognitive performance level based on thedata from the last entered epoch of sleep/wake data entered into eitherthe software or hardware. Most sleep scoring systems make the sleep/wakedetermination based on data from epochs on either side of the epochbeing analyzed. Consequently, there would be a delay in providinginformation to the user.

[0170] As one of ordinary skill in the art will appreciate from thisdescription, the described method is able to accept a continuous streamof data from either individual epochs or groups of epochs. If block_(s)of time are entered, then after initial transitions the first few epochsare governed by the appropriate transition function with the appropriatetime of solid sleep or wakefulness being used in the non-transitionfunctions.

[0171] As a feature of the invention, the sleep/wake data may comprisethe time at which a state change occurs from sleep to wake or wake tosleep. The sleep/wake data may also comprise the duration of theindividual's wake state and the duration of the individual's sleepstate. In order to generate the predicted cognitive performance curve,the sleep/wake data may be extrapolated and/or expanded into a series ofindividual epochs. As discussed above an epoch represents apredetermined length of time. Thus the sleep/wake data may be presentedin conventional units of time or may be presented in epochs. Forexample, if the sleep/wake data was sleep for 10 epochs and wake for 3epochs, in generating the cognitive performance capacities, epochs 1through 10 may represent the sleep state and epochs 11 through 13 mayrepresent the wake state.

[0172] In accordance with an aspect of the invention, the predictedcognitive performance E at a particular time q may be determined usingeither the predicted cognitive performance E or the cognitiveperformance capacity C at time r as a base point where r can be beforeor after time q. From the base point determining the cognitiveperformance capacities for the time points between times q and r wherethere is a change in state.

[0173] As shown in FIGS. 8(a)-(b), the steps are substantially the sameas the preferred embodiment with changes made to the wake and sleepfunctions, consequently the definition of the variables is the same asthe preferred embodiment except as noted. The equations described belowand the steps shown in FIGS. 8(a)-(b) are for the situation when theinitial cognitive value is prior in time to the desired predictedcognitive value. Each element of sleep/wake data is classified as eithersleep or wake.

[0174] If the sleep/wake data represents the wake state, then the impactof the task function t(t) is determined. Alternatively, the taskfunction t(t) may be modulated by the time of day function M prior tomodulating the wake function w_(m)(t) or the sleep inertia functioni(t). Next, a selection is made between two functions as to which isapplicable based on the following decision rule:

IFΔt≦k_(i)

THEN C _(t) =i(t)

ELSE C _(t) =w _(m)(t)  Equation 13

[0175] where Δt represents the amount of time in the current state,i.e., t−t_(LS). The sleep inertia function i(t) is used only if the lastdata entry is the wake state for a period of time is less than or equalto k_(i). Thus the same sleep inertia function i(t) as used in thepreferred embodiment is also used in this alternative embodiment afterbeing modulated by the task function t(t). The modified wake functionw_(m)(t) takes into account that the sleep inertia function i(t)provides a delay of k_(i) when a curve is formulated, such that after anindividual recovers from the initial suppression of cognitiveperformance capacity the individual returns to the level of cognitiveperformance capacity of the last epoch the individual was asleep priorto waking. Accounting for this delay provides the following:

w _(m)(t)=C _(t−1) −k _(w)(Δt−k _(i))  Equation 14

[0176] Alternatively, the modified wake function w_(m)(t) may begin at apoint where an undelayed w_(m)(t) intersects the sleep inertia functioni(t). The wake function w_(m)(t) is modulated by the task function t(t)under either alternative.

[0177] If the sleep/wake data represents the sleep state, then aselection is made between two functions as to which is applicable basedon the following decision rule:

IF Δt≦k_(d)

THEN C _(t) =d(t)

ELSE C _(t) =s _(m)(t)  Equation 15

[0178] The delay function d(t) is used only if the last data entry isthe sleep state for a period of time is less than or equal to k_(d).Thus the same delay function d(t) as used in the preferred embodiment isalso used in this alternative embodiment. The modified sleep functions_(m)(t) takes into account the delay function for a period of timeequal to k_(d). Accounting for the delay function d(t) provides thefollowing:

s _(m)(t)=((C _(t−1)−(k _(w) * k _(d)))+(100−(100−C _(t−1)) (1−1/k_(s))^(Δt−kd)))  Equation 16

[0179] where the first part of the equation represents the delayfunction d(t) and the second part represents the recovery of cognitiveperformance capacity C (f(t) portion of S7 c′).

[0180] A summation of the time components of the sleep/wake data isperformed as each piece of sleep/wake data is handled with respect tothe calculation of the cognitive performance capacity or prior tomodulation of the final cognitive performance capacity with the time ofday function m(t). The latter is shown in FIGS. 8(a)-(b). After the newcognitive performance capacity C_(t) is calculated, the method repeatsto handle the next piece of sleep/wake data if the present piece is notthe last piece. After the last piece the predicted cognitive performanceE is calculated based on Equation 1 above and as detailed in thepreferred embodiment.

[0181] Alternatively, the task function t(t) may be included at the sametime of the time of day function m(t) instead of for each set of wakestates by moving S9 b through S10 b to a position similar to thatillustrated in FIG. 4(b).

[0182] It should be noted again that this method includes the processesand calculations based on Equations 1 through 12 expressed in theirgeneral form, with an alternative being the removal of the task functionelements. Embodiments shall apply functions relating the variablesinvolved according to empirical knowledge, resulting in specificexpressions of those equations, as illustrated in the text and FIGS.1-4(b) above (but not confined to these), which may be changed orrefined according to the state of empirical knowledge.

APPLICATIONS OF THE INVENTION

[0183] There are a variety of potential applications of this invention.In its simplest application, the method according to the invention maybe used to predict the impact of various idealized (i.e., unfragmented)amounts of nightly sleep on predicted cognitive performance E. Anotherpractical application uses the method to predict the cognitiveperformance in an individual with fragmented sleep, either due to asleep disorder such as sleep apnea or due to environmental disturbancessuch as airplane or train noises. Another practical application uses themethod to predict the cognitive performance E of an individual changinghis/her schedule for night shift work.

[0184] In another application, the method is used to retrospectivelypredict cognitive performance E in a commercial motor vehicle operatorinvolved in a driving collision/traffic accident. In this application,the method is used first to predict an individual's level of cognitiveperformance E across some interval based on that individual's currentwork and sleep/wake schedule.

[0185] Another similar application is using the method to re-schedulesleep and wakefulness in order to optimize predicted cognitiveperformance E over an interval for a commercial motor vehicle operator.In this example, first we model a driver's predicted cognitiveperformance E based on his current sleep/wake schedule. The driver'scurrent sleep/wake schedule is generated around the maximum duty hoursallowed under the Federal Highway Administration's (FHWA)hours-of-service regulations. These regulations allow the driver toobtain a maximum 15 hours on-duty (maximum 10 hours driving plus fivehours on-duty but not driving) followed by a minimum eight hoursoff-duty. The driver may continue this on/off-duty cycling until 60hours on-duty has been accumulated—at which point the driver must taketime off until seven days has elapsed since he commenced duty. Analternative work schedule also allowed under current FHWA regulations isbased on a schedule of 12 hours on-duty and 12 hours off-duty with theunderlying assumption that the driver sleeps eight of his 12 hoursoff-duty. The use of this invention will allow the selection of theschedule that allows for maximizing the driver's cognitive performancelevels throughout a period of time.

[0186] Although described above in connection with a variety of specificactivities, this invention has many other applications. The method forpredicting cognitive performance will provide critical information formanaging both individual and group productivity. For example, inmilitary operational planning, this method will enable commanders todetermine precisely, based on prior sleep history and duties performed,each soldier's current and predicted level of cognitive performance.Commanders can also input a likely sleep/wake and work schedules andthereby predict a soldier's cognitive performance throughout animpending mission. Throughout conduct of the mission itself, the lattercognitive performance predictions (originally based on likely sleep/wakeand work schedules) can be updated based on actual sleep acquired andwork performed by an individual soldier. The ability to project futurecognitive performance will allow commanders to optimize troopperformance during continuous operations by, for example, planningsleep/wake and duty schedules around the mission to optimize cognitiveperformance, selecting those troops or combinations of troops whosepredicted cognitive performance will be maximal at a critical time, etc.This method will assist in maximizing productivity at both theindividual level and unit level.

[0187] This invention may be employed in a variety of commercialapplications covering many occupational areas for purposes of optimizingoutput (productivity). The invention provides managers with thecapability to plan operations and regulate work hours to a standardbased on objective cognitive performance predictions. This is incontrast to the frequently used method of regulating work hours by timeoff-duty (a relatively poor predictor of sleep/wake patterns andperformance of tasks, and consequently a poor predictor of cognitiveperformance) or by generating alertness/sleepiness predictions (which,as noted above, do not always correspond to cognitive performance). Theinvention can be “exercised” in hypothetical sleep/wake and dutyscenarios to provide an estimate of cognitive performance under suchscenarios. To the extent that optimizing cognitive performance is ofinterest to the general public, there is a possibility for use in avariety of applications.

[0188] This invention also may be used in conjunction with drugs toalter the sleep/wake cycle of an individual and/or optimize or minimizethe cognitive performance level of an individual as needed and/ordesired.

[0189] This invention also can work conjunctionally with the concepts ofparticle swarm theory/algorithms. Particle swarm algorithms areroutinely used to optimize the throughput of containers through a shipport or to optimize the use of workers within a work group to performtasks over a given period. An example of an application is the planningof a mission for an army unit by its commander.

[0190] The method may also be used to gauge and evaluate the cognitiveperformance effects of any biomedical, psychological, or other (e.g.,sleep hygiene, light therapy, etc.) treatments or interventions shown toimprove sleep. Examples of these include but are not limited to patientswith overt sleep disorders, circadian rhythm disorders, other medicalconditions impacting sleep quality and/or duration, poor sleep hygiene,jet lag, or any other sleep/wake problem. Currently, the efficacy oftreatments for improving sleep is determined by comparing baselinepolysomnographic measures of nighttime sleep and some measure of daytimealertness (e.g., the MSLT, the Maintenance of Wakefulness Test (MWT),the Stanford S1eepiness Scale or the Karolinska S1eepiness Scale) withthe same measures obtained after treatment. Both treatment efficacy andthe likely impact on performance during waking periods are inferred fromthe results on the daytime alertness tests. For example, the FederalAviation Administration currently requires any commercial pilotsdiagnosed with sleep apnea to undergo treatment. Such treatment isfollowed by daytime alertness testing on a modified version of the MWT.During the MWT, pilots are put in a comfortable chair in a darkened roomand instructed to try to remain awake for extended periods. If thepilots are able to avoid overt sleep under these sleep-conduciveconditions then they are deemed fit for duty. The inference is that theminimal ability to maintain wakefulness at a discrete point in timetranslates into the ability to operate an aircraft safely (i.e., it isinferred that alertness is equivalent to cognitive performance).However, sleep deprivation can affect cognitive performance even when itdoes not result in overt sleep, particularly during an alertness testwhen for various reasons the individual may be highly motivated to stayawake.

[0191] In contrast, the current method allows cognitive performance tobe estimated directly from measured sleep parameters considered inconjunction with the time of day and performance of tasks. Theadvantages of this method over current methods for evaluating treatmentefficacy are: (1) the motivations and motivation levels of the patientsbeing tested cannot affect results (cognitive performancedeterminations); and (2) the method allows numerical specification andprediction of cognitive performance across all projected waking hoursrather than indicating alertness at a discrete, specified point in time.Thus, the method provides a continuous scale for gauging cognitiveperformance across time rather than providing only a minimal “fitnessfor duty” determination based on the patient's ability to maintainEEG-defined wakefulness at a specific time.

[0192] The method may also be used clinically as an adjunct fordiagnosing sleep disorders such as narcolepsy and idiopathic CNShypersomnolence. Equally important, it may also be used to differentiateamong sleep disorders. The latter is critical to the course oftreatment, and consequent treatment efficacy depends on a valid andreliable diagnosis. For example, sleep apnea and periodic limb movementsduring sleep are characterized by nighttime sleep disruption (i.e.,partial sleep deprivation) accompanied by daytime cognitive performancedeficits. In contrast, narcolepsy and idiopathic hypersomnolence tend tobe characterized by apparently normal nighttime sleep, but accompaniedby daytime cognitive performance deficits. Based on the apparentlynormal nighttime sleep in the latter two groups, the invention wouldpredict relatively normal cognitive performance. Thus, a discrepancybetween predicted cognitive performance (based on the current invention)and observed or measured cognitive performance could be used todistinguish one sleep disorder from another. For example, narcolepsy,idiopathic hypersomnolence, or other CNS-related causes of daytimecognitive performance deficits (where no sleep deficit is apparent)could be distinguished from sleep apnea, periodic limb movements, orother causes of daytime cognitive deficits (where impaired sleep isevident).

[0193] Although the present invention has been described in terms ofparticular preferred embodiments, it is not limited to thoseembodiments. Alternative embodiments, examples, and modifications whichwould still be encompassed by the invention may be made by those skilledin the art, particularly in light of the foregoing teachings.

[0194] Furthermore, those skilled in the art will appreciate thatvarious adaptations and modifications of the above-described preferredembodiments can be configured without departing from the scope andspirit of the invention. Therefore, it is to be understood that, whithinthe scope of the appended claims, the invention may be practiced otherthan as specifically described herein.

We claim:
 1. A method for providing a cognitive performance level comprising: receiving a data series representing at least one wake state and at least one sleep state, selecting a function based on the data series, determining a cognitive performance capacity using the selected function, modulating the cognitive performance capacity with a time of day value, and providing the modulated value.
 2. The method according to claim 1, further comprising repeating the selecting, determining, and modulating steps for at least two pieces of the data series.
 3. The method according to claim 2, wherein the providing step includes displaying the modulated value to an individual located proximate to where the data series is received.
 4. The method according to claim 1, wherein the time of day value is selected from a series of time of day values representing a curve having a period of 24 hours.
 5. The method according to claim 1, wherein the selecting step selects the function from a group consisting of a wake function, a sleep function, and a sleep inertia function.
 6. The method according to claim 1, wherein the selecting step selects the function from a group consisting of a wake function, a sleep function, a delay function, and a sleep inertia function.
 7. The method according to claim 1, further comprising: storing the predicted cognitive performance level, and repeating the selecting, determining, modulating, and storing steps at least once.
 8. The method according to claim 7, further comprising plotting a curve based on the stored predicted cognitive performance levels.
 9. An apparatus for providing a cognitive performance level comprising: means for receiving a data series having at least one wake state and at least one sleep state, means for selecting a function based on the data series, means for determining a cognitive performance capacity using the selected function, means for storing a series of time of day values, means for modulating the cognitive performance capacity with a corresponding time of day value, and means for providing the modulated value.
 10. The apparatus according to claim 9, wherein the selecting means selects the function from a group consisting of a wake function, a sleep function, and a sleep inertia function.
 11. The apparatus according to claim 9, wherein the selecting means selects the function from a group consisting of a wake function, a sleep function, a delay function, and a sleep inertia function.
 12. The apparatus according to claim 9, wherein the stored time of day values represent a curve having a period of 24 hours.
 13. A method for determining a cognitive performance level comprising: inputting a data series having wake states and sleep states of an individual, selecting a function based on the wake states and sleep states in the data series, calculating a cognitive performance capacity based on the selected function, modulating the cognitive performance capacity with a time of day value, and outputting the modulated value as the predicted cognitive performance.
 14. The method according to claim 13 further comprising: storing the predicted cognitive performance, repeating the selecting, calculating, modulating and outputting steps of claim 13, plotting a curve from the stored modulated values, and outputting the curve representing cognitive performance level over time.
 15. The method according to claim 14, wherein the data series includes past information such that the curve is used to determine the cognitive level of an individual at an earlier time.
 16. The method according to claim 14, further comprising extrapolating from the curve a predictive curve based on anticipated wake states and anticipated sleep states.
 17. The method according to claim 13, wherein said outputting step includes outputting the predicted cognitive performance to a display.
 18. The method according to claim 13, wherein said outputting step includes outputting the predicted cognitive performance to a data file.
 19. The method according to claim 13, wherein said outputting step includes outputting the predicted cognitive performance to a printing device.
 20. The method according to claim 13, further comprising formulating the time of day values to represent a curve having a period of 24 hours.
 21. The method according to claim 20, wherein the curve includes a first sinusoidal curve having a 24-hour period and a second sinusoidal curve having a 12hour period.
 22. The method according to claim 13, wherein the time of day values represent a curve having a period of 24 hours.
 23. The method according to claim 13, wherein the data series is obtained from a device attached to the individual.
 24. The method according to claim 13, wherein the data series is an output of a sleep scoring system.
 25. The method according to claim 13, wherein the selecting step selects from a group consisting of a wake function, a sleep function, a delay function, and a sleep inertia function.
 26. The method according to claim 13, wherein the selecting step selects from a group consisting of a wake function, a sleep function, and a sleep inertia function.
 27. The method according to claim 13, wherein the selecting step includes determining the present state for the data series as either a wake state or a sleep state, calculating a length of time in the present state, and selecting the function based on the length of time in the present state and the present state.
 28. The method according to claim 13, wherein the first calculating step calculates a cognitive performance level as a percentage value such that 100% is a maximum cognitive performance capacity.
 29. A method for determining a predicted cognitive performance comprising: inputting a data series having wake states and sleep states of an individual, selecting a function based on the wake states and sleep states in the data series, calculating a cognitive performance capacity based on the selected function, approximating a first curve of calculated cognitive performance capacities, modulating the first curve with a second curve representing time of day rhythms, and outputting the modulated first curve representing the predicted cognitive performance.
 30. The method according to claim 29, wherein said outputting step includes outputting a value of a point on the modulated first curve to a display.
 31. The method according to claim 29, wherein said outputting step includes outputting a value of a point on the modulated first curve to a data file.
 32. The method according to claim 29, wherein said outputting step includes outputting a value of a point on the modulated first curve to a printing device.
 33. The method according to claim 29, further comprising extrapolating from the modulated first curve a predictive curve based on anticipated wake states and anticipated sleep states.
 34. The method according to claim 29, further comprising formulating the second curve having a period of 24 hours such that the second curve includes a first sinusoidal curve having a 24-hour period and a second sinusoidal curve having a 12hour period.
 35. The method according to claim 29, wherein the second curve has a period of 24 hours such that the second curve includes a first sinusoidal curve having a 24hour period and a second sinusoidal curve having a 12-hour period.
 36. The method according to claim 29, wherein the data series is obtained from a device attached to the individual.
 37. The method according to claim 29, wherein the data series is an output of a sleep scoring system.
 38. The method according to claim 29, wherein the selecting step selects from a group consisting of a wake function, a sleep function, a delay function, and a sleep inertia function.
 39. The method according to claim 29, wherein the selecting step includes: determining the present state for the data series as either the wake state or the sleep state, calculating a length of time in the present state, and selecting the function based on the length of time in the present state and the present state.
 40. The method according to claim 29, wherein the calculating step includes calculating a cognitive performance level as a percentage value such that 100% is a maximum cognitive performance capacity. 